According to the literature norm [1]}, [2]}, [3]}, CTC decomposes the target sequence into unigram character-level target units and subsequently, at the decoding process, the transcribed text is going to be synthesized by them.
Following this reasoning, any sequence of characters can be formed by the decoding process, even if it does not correspond to a valid word.
Such unconstrained decoding can be a potentially strong disadvantage and, thus, it is widely common to enhance the decoding step by fusing language knowledge from an external source.
Specifically, statistical Language Models (LMs) are employed, by utilizing the occurring frequencies of character- and word-level n-grams as priors in the decoding process.
In other words, LMs penalize scarcely found sub-sequences of characters that may correspond to artifacts of the visual recognition step. There are several research direction that investigate decoding [4]}.
